Beyond Endpoint Gains: A Weight-Delta Audit of Medical Specialization

2026-08-21

AI Summary

本文提出一种配对权重增量路径审计方法，应用于两个通用到医疗专业模型的更新过程，分析其在医学基准测试中的表现变化。

Abstract

Specialist language models are usually understood through endpoint gains: the generalist scores lower, the specialist scores higher, and the difference is treated as evidence of specialization. This leaves the released update itself largely unexamined. We propose a paired weight-delta path audit and apply it to two public, aligned generalist-to-medical-specialist checkpoint pairs: Gemma-3-4B-IT to MedGemma-4B-IT and Qwen2.5-7B-Instruct to HuatuoGPT-o1-7B. In both pairs, the full decoder-side update strongly reconstructs measured medical benchmark movement (0.974 and 1.183 endpoint-normalized retention), making each decoder delta an appropriate substrate for the audit. Yet the movement is not cleanly localized. MLP is the strongest broad component family in both pairs, but mixed off-domain movements, 10-seed matched controls, and endpoint-anchored rollbacks prevent a unique coarse-family explanation. The audit therefore separates update-level reconstruction from component-level explanation. Its claims concern text-only multiple-choice benchmark movement, not clinical validation, repair, or circuit-level mechanism.

Problem

• specialist language models
• endpoint gains
• weight-delta audit

Innovation

• weight-delta path audit
• medical specialization
• decoder-side update
• MLP component